Research within the agricultural community has shown that management of crop production may be optimized by taking into account spatial variations that often exist within a given farming field. For example, by varying the farming inputs applied to a field according to local conditions within the field, a farmer can optimize crop yield as a function of the inputs being applied while preventing or minimizing environmental damage. This management technique has become known as precision, site-specific, prescription or spatially-variable farming and is becoming increasingly popular among the agricultural community.
The management of a field using precision farming techniques requires the gathering and processing of georeferenced data relating to site-specific characteristics of the field (i.e., georeferenced characteristic data). Georeferenced data refers to data having position information relative to the earth (e.g., latitude and longitude information) or position information relative to an object on or in the earth (e.g., an antenna or other nearby object). The georeferenced characteristic data may be obtained by, for example, manual measuring or sensing during field operations. A farmer may take manual measurements by visually noting characteristics of a field (e.g., insect infestation) and recording the position as he traverses the field, or by taking soil samples and analyzing them in a laboratory. Sensing during field operations involves using appropriate sensors supported by a combine, tractor or other vehicle.
Georeferenced characteristic data may relate to the local conditions of the field, farming inputs applied to the field, or crops harvested from the field. For example, the gathered data may represent soil properties (e.g., soil type, soil fertility, soil moisture content, soil compaction or pH), crop properties (e.g., height, crop moisture content or yield), or farming inputs applied to the field (e.g., fertilizers, herbicides, water, insecticides, seeds, cultural practices or tillage techniques used). Other site-specific data may represent insect or weed infestation, landmarks, or topography (e.g., altitude). This data may then be stored in a Geographic Information System ("GIS", e.g., ARC/INFO, MapInfo, Agri-Logic Instant Yield Map or Spatial Database Engine by ESRI) for further processing either on the vehicle or at a remote computer.
The analysis of georeferenced characteristic data is a complex task requiring a knowledge base of the relationships between sensed field characteristics and the related needs of the field. A precursor to this analysis is a derivation of the boundary around the georeferenced characteristic data, i.e., the field boundary, The field boundary is required for the farmer to, inter alia, map out one or more of the fields in the farm, allow calculations of total field area and average yield or farming inputs applied, and allow prescription maps to be generated.
Several methods have been implemented for determining the field boundary. In one example, the farmer graphically views a set of location points from harvest data and "connects the dots" via a suitable graphical user interface to determine the field boundary. However, for data set including on the order of 50,000 points or more, this method is time-consuming and requires expensive graphical user interface equipment. Another proposed solution is to traverse the boundary of the field in a vehicle before farming and utilize a global positioning system (GPS) receiver to record location information for the field boundary. After traversing the boundary of the field, consecutive location points are connected to determine the field boundary. This solution, however, carries with it significant expenditures in time and resources that could be spent working the field. In the event an error occurs at some point in the boundary traversal (e.g., due to loss of satellite lock, loss of differential signal in a differential GPS system, or other error), these expenditures could be even more significant.
Accordingly, what is needed is a reliable system and method for deriving the field boundary of an agricultural field that neither requires the farmer to connect dots nor requires the farmer to traverse the field boundary in a vehicle prior to farming. What is also needed is a system and method for deriving the field boundary of an agricultural field that gives the farmer flexibility in deriving the actual field boundary based on a set of points, that more accurately matches the field boundary to the true "shape" of the field, and that can accurately derive concave as well as convex, and other various unusual portions of the field boundary.